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Area of Science:

  • Social Psychology
  • Quantitative Psychology
  • Statistics

Background:

  • The Actor-Partner Interdependence Model (APIM) is a standard for analyzing dyadic data.
  • Traditional APIM applications are limited to single manifest variables, restricting analysis of complex constructs.
  • There is a need for methods accommodating multivariate dyadic data reflecting latent constructs.

Purpose of the Study:

  • To extend the Actor-Partner Interdependence Model (APIM) for multivariate dyadic data.
  • To introduce and evaluate three APIM extensions: manifest, composite-score, and latent APIM.
  • To compare the performance of these extensions in analyzing dyadic patterns.

Main Methods:

  • Development of three multivariate APIM extensions: manifest, composite-score, and latent.
  • A simulation study to investigate the properties and performance of each method.
  • Application of the extensions to a real-world dataset on relationship commitment and happiness.

Main Results:

  • The latent APIM demonstrated adequate estimation of dyadic relationships and reasonable statistical power with reliable measures.
  • The manifest APIM showed poor power and high Type I error rates.
  • The composite-score APIM, while better than the manifest APIM, did not accurately capture latent interdependence.

Conclusions:

  • The latent APIM is the preferred method for analyzing multivariate dyadic data when measures reliably reflect psychological constructs.
  • The manifest and composite-score APIMs present inferential limitations.
  • Emphasizes the critical importance of rigorous measurement model examination in dyadic data analysis.